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	---
	language: []
	library_name: sentence-transformers
	tags:
	- mteb
	- sentence-transformers
	- sentence-similarity
	- feature-extraction
	- dataset_size:100K<n<1M
	- loss:AnglELoss
	base_model: sentence-transformers/paraphrase-multilingual-mpnet-base-v2
	widget:
	- source_sentence: 有些人在路上溜达。
	sentences:
	- Folk går
	- Otururken gitar çalan adam.
	- ארה"ב קבעה שסוריה השתמשה בנשק כימי
	- source_sentence: 緬甸以前稱為緬甸。
	sentences:
	- 缅甸以前叫缅甸。
	- This is very contradictory.
	- 한 남자가 아기를 안고 의자에 앉아 잠들어 있다.
	- source_sentence: אדם כותב.
	sentences:
	- האדם כותב.
	- questa non è una risposta.
	- 7 שוטרים נהרגו ו-4 שוטרים נפצעו.
	- source_sentence: הם מפחדים.
	sentences:
	- liên quan đến rủi ro đáng kể;
	- A man is playing a guitar.
	- A man is playing a piano.
	- source_sentence: 一个女人正在洗澡。
	sentences:
	- A woman is taking a bath.
	- En jente børster håret sitt
	- אדם מחלק תפוח אדמה.
	pipeline_tag: sentence-similarity
	---

	## State-of-the-Art Results Comparison (MTEB STS Multilingual Leaderboard)

	\| Dataset \| State-of-the-art (Multi) \| STSb-XLM-RoBERTa-base \| STS Multilingual MPNet base v2 \|
	\|-------------------\|--------------------------\|-----------------------\|--------------------------------------\|
	\| Average \| 73.17 \| 71.68 \| 73.89 \|
	\| STS17 (ar-ar) \| 81.87 \| 80.43 \| 81.24 \|
	\| STS17 (en-ar) \| 81.22 \| 76.3 \| 77.03 \|
	\| STS17 (en-de) \| 87.3 \| 91.06 \| 91.09 \|
	\| STS17 (en-tr) \| 77.18 \| 80.74 \| 79.87 \|
	\| STS17 (es-en) \| 88.24 \| 83.09 \| 85.53 \|
	\| STS17 (es-es) \| 88.25 \| 84.16 \| 87.27 \|
	\| STS17 (fr-en) \| 88.06 \| 91.33 \| 90.68 \|
	\| STS17 (it-en) \| 89.68 \| 92.87 \| 92.47 \|
	\| STS17 (ko-ko) \| 83.69 \| 97.67 \| 97.66 \|
	\| STS17 (nl-en) \| 88.25 \| 92.13 \| 91.15 \|
	\| STS22 (ar) \| 58.67 \| 58.67 \| 62.66 \|
	\| STS22 (de) \| 60.12 \| 52.17 \| 57.74 \|
	\| STS22 (de-en) \| 60.92 \| 58.5 \| 57.5 \|
	\| STS22 (de-fr) \| 67.79 \| 51.28 \| 57.99 \|
	\| STS22 (de-pl) \| 58.69 \| 44.56 \| 44.22 \|
	\| STS22 (es) \| 68.57 \| 63.68 \| 66.21 \|
	\| STS22 (es-en) \| 78.8 \| 70.65 \| 75.18 \|
	\| STS22 (es-it) \| 75.04 \| 60.88 \| 66.25 \|
	\| STS22 (fr) \| 83.75 \| 76.46 \| 78.76 \|
	\| STS22 (fr-pl) \| 84.52 \| 84.52 \| 84.52 \|
	\| STS22 (it) \| 79.28 \| 66.73 \| 68.47 \|
	\| STS22 (pl) \| 42.08 \| 41.18 \| 43.36 \|
	\| STS22 (pl-en) \| 77.5 \| 64.35 \| 75.11 \|
	\| STS22 (ru) \| 61.71 \| 58.59 \| 58.67 \|
	\| STS22 (tr) \| 68.72 \| 57.52 \| 63.84 \|
	\| STS22 (zh-en) \| 71.88 \| 60.69 \| 65.37 \|
	\| STSb \| 89.86 \| 95.05 \| 95.15 \|

	Bold indicates the best result in each row.

	# SentenceTransformer based on sentence-transformers/paraphrase-multilingual-mpnet-base-v2

	This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/paraphrase-multilingual-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-mpnet-base-v2). It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.

	## Model Details

	### Model Description
	- Model Type: Sentence Transformer
	- Base model: [sentence-transformers/paraphrase-multilingual-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-mpnet-base-v2) <!-- at revision 79f2382ceacceacdf38563d7c5d16b9ff8d725d6 -->
	- Maximum Sequence Length: 128 tokens
	- Output Dimensionality: 768 tokens
	- Similarity Function: Cosine Similarity
	<!-- - Training Dataset: Unknown -->
	<!-- - Language: Unknown -->
	<!-- - License: Unknown -->

	### Model Sources

	- Documentation: [Sentence Transformers Documentation](https://sbert.net)
	- Repository: [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
	- Hugging Face: [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)

	### Full Model Architecture

	```
	SentenceTransformer(
	(0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: XLMRobertaModel
	(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
	)
	```

	## Usage

	### Direct Usage (Sentence Transformers)

	First install the Sentence Transformers library:

	```bash
	pip install -U sentence-transformers
	```

	Then you can load this model and run inference.
	```python
	from sentence_transformers import SentenceTransformer

	# Download from the 🤗 Hub
	model = SentenceTransformer("Gameselo/STS-multilingual-mpnet-base-v2")
	# Run inference
	sentences = [
	'一个女人正在洗澡。',
	'A woman is taking a bath.',
	'En jente børster håret sitt',
	]
	embeddings = model.encode(sentences)
	print(embeddings.shape)
	# [3, 768]

	# Get the similarity scores for the embeddings
	similarities = model.similarity(embeddings, embeddings)
	print(similarities.shape)
	# [3, 3]
	```

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	## Evaluation

	### Metrics

	#### Semantic Similarity
	* Dataset: `sts-dev`
	* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)

	\| Metric \| Value \|
	\|:--------------------\|:-----------\|
	\| pearson_cosine \| 0.9551 \|
	\| spearman_cosine \| 0.9593 \|
	\| pearson_manhattan \| 0.927 \|
	\| spearman_manhattan \| 0.9383 \|
	\| pearson_euclidean \| 0.9278 \|
	\| spearman_euclidean \| 0.9394 \|
	\| pearson_dot \| 0.876 \|
	\| spearman_dot \| 0.8865 \|
	\| pearson_max \| 0.9551 \|
	\| spearman_max \| 0.9593 \|

	#### Evalutation results vs SOTA results
	* Dataset: `sts-test`
	* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)

	\| Metric \| Value \|
	\|:--------------------\|:-----------\|
	\| pearson_cosine \| 0.948 \|
	\| spearman_cosine \| 0.9515 \|
	\| pearson_manhattan \| 0.9252 \|
	\| spearman_manhattan \| 0.9352 \|
	\| pearson_euclidean \| 0.9258 \|
	\| spearman_euclidean \| 0.9364 \|
	\| pearson_dot \| 0.8443 \|
	\| spearman_dot \| 0.8435 \|
	\| pearson_max \| 0.948 \|
	\| spearman_max \| 0.9515 \|

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	## Bias, Risks and Limitations

	What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.
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	## Training Details

	### Training Dataset

	#### Unnamed Dataset


	* Size: 226,547 training samples
	* Columns: <code>sentence_0</code>, <code>sentence_1</code>, and <code>label</code>
	* Approximate statistics based on the first 1000 samples:
	\| \| sentence_0 \| sentence_1 \| label \|
	\|:--------\|:-----------------------------------------------------------------------------------\|:-----------------------------------------------------------------------------------\|:-----------------------------------------------------------------\|
	\| type \| string \| string \| float \|
	\| details \| <ul><li>min: 3 tokens</li><li>mean: 20.05 tokens</li><li>max: 128 tokens</li></ul> \| <ul><li>min: 4 tokens</li><li>mean: 19.94 tokens</li><li>max: 128 tokens</li></ul> \| <ul><li>min: 0.0</li><li>mean: 1.92</li><li>max: 398.6</li></ul> \|
	* Samples:
	\| sentence_0 \| sentence_1 \| label \|
	\|:-------------------------------------------------------------------\|:----------------------------------------------------------------\|:---------------------------------\|
	\| <code>Bir kadın makineye dikiş dikiyor.</code> \| <code>Bir kadın biraz et ekiyor.</code> \| <code>0.12</code> \|
	\| <code>Snowden 'gegeven vluchtelingendocument door Ecuador'.</code> \| <code>Snowden staat op het punt om uit Moskou te vliegen</code> \| <code>0.24000000953674316</code> \|
	\| <code>Czarny pies idzie mostem przez wodę</code> \| <code>Czarny pies nie idzie mostem przez wodę</code> \| <code>0.74000000954</code> \|
	* Loss: [<code>AnglELoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#angleloss) with these parameters:
	```json
	{
	"scale": 20.0,
	"similarity_fct": "pairwise_angle_sim"
	}
	```

	### Training Hyperparameters
	#### Non-Default Hyperparameters

	- `per_device_train_batch_size`: 256
	- `per_device_eval_batch_size`: 256
	- `num_train_epochs`: 10
	- `multi_dataset_batch_sampler`: round_robin

	#### All Hyperparameters
	<details><summary>Click to expand</summary>

	- `overwrite_output_dir`: False
	- `do_predict`: False
	- `prediction_loss_only`: True
	- `per_device_train_batch_size`: 256
	- `per_device_eval_batch_size`: 256
	- `per_gpu_train_batch_size`: None
	- `per_gpu_eval_batch_size`: None
	- `gradient_accumulation_steps`: 1
	- `eval_accumulation_steps`: None
	- `learning_rate`: 5e-05
	- `weight_decay`: 0.0
	- `adam_beta1`: 0.9
	- `adam_beta2`: 0.999
	- `adam_epsilon`: 1e-08
	- `max_grad_norm`: 1
	- `num_train_epochs`: 10
	- `max_steps`: -1
	- `lr_scheduler_type`: linear
	- `lr_scheduler_kwargs`: {}
	- `warmup_ratio`: 0.0
	- `warmup_steps`: 0
	- `log_level`: passive
	- `log_level_replica`: warning
	- `log_on_each_node`: True
	- `logging_nan_inf_filter`: True
	- `save_safetensors`: True
	- `save_on_each_node`: False
	- `save_only_model`: False
	- `no_cuda`: False
	- `use_cpu`: False
	- `use_mps_device`: False
	- `seed`: 42
	- `data_seed`: None
	- `jit_mode_eval`: False
	- `use_ipex`: False
	- `bf16`: False
	- `fp16`: False
	- `fp16_opt_level`: O1
	- `half_precision_backend`: auto
	- `bf16_full_eval`: False
	- `fp16_full_eval`: False
	- `tf32`: None
	- `local_rank`: 0
	- `ddp_backend`: None
	- `tpu_num_cores`: None
	- `tpu_metrics_debug`: False
	- `debug`: []
	- `dataloader_drop_last`: False
	- `dataloader_num_workers`: 0
	- `dataloader_prefetch_factor`: None
	- `past_index`: -1
	- `disable_tqdm`: False
	- `remove_unused_columns`: True
	- `label_names`: None
	- `load_best_model_at_end`: False
	- `ignore_data_skip`: False
	- `fsdp`: []
	- `fsdp_min_num_params`: 0
	- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
	- `fsdp_transformer_layer_cls_to_wrap`: None
	- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'gradient_accumulation_kwargs': None}
	- `deepspeed`: None
	- `label_smoothing_factor`: 0.0
	- `optim`: adamw_torch
	- `optim_args`: None
	- `adafactor`: False
	- `group_by_length`: False
	- `length_column_name`: length
	- `ddp_find_unused_parameters`: None
	- `ddp_bucket_cap_mb`: None
	- `ddp_broadcast_buffers`: False
	- `dataloader_pin_memory`: True
	- `dataloader_persistent_workers`: False
	- `skip_memory_metrics`: True
	- `use_legacy_prediction_loop`: False
	- `push_to_hub`: False
	- `resume_from_checkpoint`: None
	- `hub_model_id`: None
	- `hub_strategy`: every_save
	- `hub_private_repo`: False
	- `hub_always_push`: False
	- `gradient_checkpointing`: False
	- `gradient_checkpointing_kwargs`: None
	- `include_inputs_for_metrics`: False
	- `eval_do_concat_batches`: True
	- `fp16_backend`: auto
	- `push_to_hub_model_id`: None
	- `push_to_hub_organization`: None
	- `mp_parameters`:
	- `auto_find_batch_size`: False
	- `full_determinism`: False
	- `torchdynamo`: None
	- `ray_scope`: last
	- `ddp_timeout`: 1800
	- `torch_compile`: False
	- `torch_compile_backend`: None
	- `torch_compile_mode`: None
	- `dispatch_batches`: None
	- `split_batches`: None
	- `include_tokens_per_second`: False
	- `include_num_input_tokens_seen`: False
	- `neftune_noise_alpha`: None
	- `optim_target_modules`: None
	- `batch_sampler`: batch_sampler
	- `multi_dataset_batch_sampler`: round_robin

	</details>

	### Training Logs
	\| Epoch \| Step \| Training Loss \| sts-dev_spearman_cosine \| sts-test_spearman_cosine \|
	\|:------:\|:----:\|:-------------:\|:-----------------------:\|:------------------------:\|
	\| 0.5650 \| 500 \| 10.9426 \| - \| - \|
	\| 1.0 \| 885 \| - \| 0.9202 \| - \|
	\| 1.1299 \| 1000 \| 9.7184 \| - \| - \|
	\| 1.6949 \| 1500 \| 9.5348 \| - \| - \|
	\| 2.0 \| 1770 \| - \| 0.9400 \| - \|
	\| 2.2599 \| 2000 \| 9.4412 \| - \| - \|
	\| 2.8249 \| 2500 \| 9.3097 \| - \| - \|
	\| 3.0 \| 2655 \| - \| 0.9489 \| - \|
	\| 3.3898 \| 3000 \| 9.2357 \| - \| - \|
	\| 3.9548 \| 3500 \| 9.1594 \| - \| - \|
	\| 4.0 \| 3540 \| - \| 0.9528 \| - \|
	\| 4.5198 \| 4000 \| 9.0963 \| - \| - \|
	\| 5.0 \| 4425 \| - \| 0.9553 \| - \|
	\| 5.0847 \| 4500 \| 9.0382 \| - \| - \|
	\| 5.6497 \| 5000 \| 8.9837 \| - \| - \|
	\| 6.0 \| 5310 \| - \| 0.9567 \| - \|
	\| 6.2147 \| 5500 \| 8.9403 \| - \| - \|
	\| 6.7797 \| 6000 \| 8.8841 \| - \| - \|
	\| 7.0 \| 6195 \| - \| 0.9581 \| - \|
	\| 7.3446 \| 6500 \| 8.8513 \| - \| - \|
	\| 7.9096 \| 7000 \| 8.81 \| - \| - \|
	\| 8.0 \| 7080 \| - \| 0.9582 \| - \|
	\| 8.4746 \| 7500 \| 8.8069 \| - \| - \|
	\| 9.0 \| 7965 \| - \| 0.9589 \| - \|
	\| 9.0395 \| 8000 \| 8.7616 \| - \| - \|
	\| 9.6045 \| 8500 \| 8.7521 \| - \| - \|
	\| 10.0 \| 8850 \| - \| 0.9593 \| 0.6266 \|


	### Framework Versions
	- Python: 3.9.7
	- Sentence Transformers: 3.0.0
	- Transformers: 4.40.1
	- PyTorch: 2.3.0+cu121
	- Accelerate: 0.29.3
	- Datasets: 2.19.0
	- Tokenizers: 0.19.1

	## Citation

	### BibTeX

	#### Sentence Transformers
	```bibtex
	@inproceedings{reimers-2019-sentence-bert,
	title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
	author = "Reimers, Nils and Gurevych, Iryna",
	booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
	month = "11",
	year = "2019",
	publisher = "Association for Computational Linguistics",
	url = "https://arxiv.org/abs/1908.10084",
	}
	```

	#### AnglELoss
	```bibtex
	@misc{li2023angleoptimized,
	title={AnglE-optimized Text Embeddings},
	author={Xianming Li and Jing Li},
	year={2023},
	eprint={2309.12871},
	archivePrefix={arXiv},
	primaryClass={cs.CL}
	}
	```

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